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Stochastic Processes and Filtering Theory.
This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattere...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington :
Elsevier Science,
1970.
|
| Colección: | Mathematics in science and engineering.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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|---|---|---|---|
| 001 | EBSCO_ocn990540221 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
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| 008 | 140113s1970 vtu o 000 0 eng d | ||
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| 020 | |a 9780123815507 | ||
| 024 | 3 | |a 9780123815507 | |
| 029 | 1 | |a CHNEW |b 001014354 | |
| 035 | |a (OCoLC)990540221 |z (OCoLC)944974434 |z (OCoLC)1264858529 |z (OCoLC)1311348904 |z (OCoLC)1311349193 | ||
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| 082 | 0 | 4 | |a 519.1 |a 519.544 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Jazwinski, Andrew H. | |
| 245 | 1 | 0 | |a Stochastic Processes and Filtering Theory. |
| 260 | |a Burlington : |b Elsevier Science, |c 1970. | ||
| 300 | |a 1 online resource (391 pages). | ||
| 336 | |a text |b txt | ||
| 337 | |a computer |b c | ||
| 338 | |a online resource |b cr | ||
| 490 | 1 | |a Mathematics in Science and Engineering | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Front Cover; Stochastic Processes and Filtering Theory; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1 Introduction; 1. Introduction; 2. Scope and Objectives; 3. A Guided Tour; References; Chapter 2 Probability Theory and Random Variables; 1. Introduction; 2. Probability Axioms; 3. Random Variables; 4. Jointly Distributed Random Variables; 5. Conditional Probabilities and Expectations; 6. Properties of Gaussian Random Variables; References; Chapter 3 Stochastic Processes; 1. Introduction; 2. Probability Law of a Stochastic Process; 3. Convergence of Random Sequences. | |
| 505 | 8 | |a 4. Mean Square Calculus5. Independence, Conditioning, the Brownian Motion Process; 6. Gaussian Processes; 7. Markov Processes; 8. White Noise; 9. Stochastic Difference Equations; References; Chapter 4 Stochastic Differential Equations; 1. Introduction; 2. Modeling the Process eßt, ; 3. Itô Stochastic Integral; 4. Stochasjic Differential Equations; 5. Itô Stochastic Calculus; 6. Stochastic Integral of Stratonovich; 7. Evaluation of Stochastic (Ito) Integrals; 8. More on Modeling; 9. Kolmogorov's Equations; 10. Discussion; References; Chapter 5 Introduction to Filtering Theory; 1. Introduction. | |
| 505 | 8 | |a 2. Probabilistic Approach3. Statistical Methods; 4. Foreword and Discussion; References; Chapter 6 Nonlinear Filtering Theory; 1. Introduction; 2. Continuous-Discrete Filtering; 3. Evolution of the Conditional Density (Continuous-Discrete); 4. Evolution of Moments (Continuous-Discrete); 5. Evolution of the Mode (Continuous-Discrete); 6. Discrete Filtering; 7. Continuous Filtering; 8. Evolution of the Conditional Density (Continuous); 9. Evolution of Moments (Continuous); 10. Evolution of the Mode (Continuous); 11. Limited Memory Filter; References; Chapter 7 Linear Filtering Theory. | |
| 505 | 8 | |a 1. Introduction2. Continuous-Discrete Filter; 3. Discrete Filter; 4. Continuous Filter; 5. Observability and Information; 6. Bounds and Stability-Discrete Filter; 7. Bounds and Stability-Continuous Filter; 8. Error Sensitivity-Discrete Filter; 9. Error Sensitivity-Continuous Filter; 10. Linear Limited Memory Filter; Appendix 7A Classical Parameter Estimation; Appendix 7B Some Matrix Equalities; References; Chapter 8 Applications of Linear Theory; 1. Introduction; 2. Review of the Discrete Filter; 3. Extension to Nonlinear Problems; 4. Uncertain Parameters; 5. A Simple Example. | |
| 505 | 8 | |a 6. Applications in Orbit Determination7. Applications in Reentry; 8. Filter Divergence and Error Compensation Techniques; 9. Fictitious Noise Inputs and Parameter Uncertainties; 10. Overweighting the Most Recent Data; 11. Adaptive Noise Estimation; 12. Limited Memory Filtering; 13. Miscellaneous Topics; Appendix 8A Orbit Mechanics; Appendix 8B Reentry Mechanics; References; Chapter 9 Approximate Nonlinear Filters; 1. Introduction; 2. Approximation Techniques: Parametrization of Density Functions; 3. Nonlinear Filter Approximations: Continuous Filter. | |
| 500 | |a 4. Nonlinear Filter Approximations: Continuous-Discrete Filter. | ||
| 520 | |a This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been collected in a single source. Second, available literature on the continuous nonlinear theory is quite esoteric and controversial, and thus inaccessible to engineers uninitiated in measure theory and stochastic differential equations. Furthermore, it is n. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Estimation theory. | |
| 650 | 0 | |a Stochastic processes. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 2 | |a Stochastic Processes | |
| 650 | 2 | |a Mathematics | |
| 650 | 4 | |a Bayesian statistical decision. | |
| 650 | 4 | |a Bayesian statistical decision theory. | |
| 650 | 4 | |a Estimation theory. | |
| 650 | 4 | |a Filtering. | |
| 650 | 4 | |a Stochastic processes. | |
| 650 | 4 | |a Mathematical Statistics. | |
| 650 | 4 | |a Mathematics. | |
| 650 | 4 | |a Physical Sciences & Mathematics. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Théorie de l'estimation. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 6 | |a Mathématiques. | |
| 650 | 7 | |a mathematics. |2 aat | |
| 650 | 7 | |a applied mathematics. |2 aat | |
| 650 | 7 | |a Mathematics |2 fast | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Estimation theory |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
| 830 | 0 | |a Mathematics in science and engineering. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297093 |z Texto completo |
| 994 | |a 92 |b IZTAP | ||